Identification of Circulating Serum miRNAs as Novel Biomarkers in Pancreatic Cancer Using a Penalized Algorithm

Diagnostic and Prognostic Accuracy of MiRNAs in Pancreatic Cancer: A Systematic Review and Meta-Analysis

Pancreatic cancer (PC) remains a significant contributor to global cancer mortality, with limited effective diagnostic and prognostic tools. MicroRNAs (miRNAs) have emerged as promising biomarkers for PC diagnosis and prognosis. A comprehensive literature search was conducted in PubMed, Web of Science, and Scopus. Studies reporting sensitivity, specificity or area under the curve (AUC) for miRNAs in PC diagnosis, as well as hazard ratios (HRs) for survival evaluations, were included. Data extraction and quality assessment followed PRISMA guidelines. Meta-analyses were conducted using appropriate statistical methods. The protocol is registered in PROSPERO. Diagnostic analysis included 290 evaluations, revealing an overall AUC of 0.8226 for PC diagnosis. Subgroup analyses showed varying accuracies, with blood and tissue specimens yielding higher AUC values. Promising miRNAs with AUC values above 0.8 included miR-320, miR-1290, miR-93, miR-25, miR-451, miR-20, miR-21, miR-223 and miR-122. Prognostic analysis encompassed 46 studies, indicating significant associations between miRNA expression and overall survival (OS) and progression-free survival (PFS). The combined HR for studies reporting OS HRs higher than one was 1.7613 (95% CI: 1.5394-2.0152, p < 0.0001; I2 = 81.7%). Notable miRNAs with prognostic significance included miR-10, miR-21 and miR-221. Studies reporting OS HRs less than one had a pooled HR of 0.6805 (95% CI: 0.5862-0.7901, p < 0.0001; I2 = 65.4%). MiRNAs hold promise as diagnostic and prognostic biomarkers for PC. Blood and tissue specimens offer superior diagnostic accuracy, and several miRNAs show potential for predicting patient outcomes.

Differential circulating miRNA profiles identified miR-423-5p, miR-93-5p, and miR-4532 as potential biomarkers for cholangiocarcinoma diagnosis

Background

Cholangiocarcinoma (CCA) is high in morbidity and mortality rates which may be due to asymptomatic and effective diagnostic methods not available. Therefore, an effective diagnosis is urgently needed.

Methods

Investigation of plasma circulating miRNA (cir-miRNA) was divided into two phases, including the discovery phase (pooled 10 samples each from three pools in each group) and the validation phase (17, 16, and 35 subjects of healthy control (HC), O. viverrini (OV), and CCA groups, respectively). The plasma from healthy control subjects, O. viverrini infected subjects, and CCA subjects was used. In the discovery phase, plasma was pooled by adding an equal volume of plasma, and cir-miRNA was isolated and analyzed with the nCounter® SPRINT Profiler. The significantly different cir-miRNAs were selected for the validation phase. In the validation phase, cir-miRNA was isolated and analyzed using real time-quantitative polymerase chain reaction (RT-qPCR). Subsequently, statistical analysis was conducted, and diagnostic parameters were calculated.

Results

Differential plasma cir-miRNA profile showed at least three candidates including miR-423-5p, miR-93-5p, and miR-4532 as potential biomarkers. From validation of these cir-miRNAs by RT-qPCR, the result showed that the satisfied sensitivity and specificity to differential CCA group from HC and OV group was obtained from miR-4532 (P < 0.05) while miR-423-5p and miR-93-5p can be used for differential CCA from OV and HC group (P < 0.05) with high specificity but limited the sensitivity. In conclusion, candidate cir-miRNAs have been identified as potential biomarkers including miR-423-5p, miR-93-5p and miR-4532. Screening by miR-4532 and confirmed with miR-423-5p, miR-93-5p were suggested for differential CCA patients in the endemic area of O. viverrini.

miR-5100 Overexpression Inhibits Prostate Cancer Progression by Inducing Cell Cycle Arrest and Targeting E2F7

Despite advances in treatment, prostate cancer remains a leading cause of cancer-related deaths among men, highlighting the urgent need for innovative therapeutic strategies. MicroRNAs (miRNAs) have emerged as key regulatory molecules in cancer biology. In this research, we investigated the tumor-suppressive role of miR-5100 in PCa and its underlying molecular mechanism. By using RT-qPCR, we observed lower miR-5100 expression in PCa cell lines than in benign prostate cells. Functional assays demonstrated that miR-5100 overexpression significantly suppressed PCa cell proliferation, migration, and invasion. By using RNA-sequencing, we identified 446 down-regulated and 806 upregulated candidate miR-5100 target genes overrepresenting cell cycle terms. Mechanistically, E2F7 was confirmed as a direct target of miR-5100 using the reporter gene assay and RIP assay. By conducting flow cytometry analysis, cell cycle progression was blocked at the S phase. E2F7 overexpression partially mitigated the suppressive impact of miR-5100 in PCa cells. In conclusion, miR-5100 is a tumor suppressor in PCa by blocking cell cycle and targeting E2F7.

Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration

PURPOSE

Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and patient outcomes. These biomarkers can potentially revolutionize medical practice by enabling personalized, more effective, targeted treatments, ultimately improving patient outcomes.

METHODS

The search strategy was developed following PRISMA guidelines. A comprehensive search was performed across four electronic databases: PubMed, Scopus, EMBASE, and Web of Science, covering all English publications up to September 2022. The Newcastle-Ottawa Scale (NOS) was utilized to assess bias, categorizing studies as "good," "fair," or "poor" quality based on their NOS scores. Descriptive statistics for all included studies were compiled and reviewed, along with the NOS scores for each study to indicate their quality assessment.

RESULTS

Our results showed that SVM and RF are the most widely used algorithms in machine learning and data analysis, particularly for biomarker identification. SVM, a supervised learning algorithm, is employed for both classification and regression by mapping data points in high-dimensional space to identify the optimal separating hyperplane between classes.

CONCLUSIONS

The application of machine-learning algorithms in the search for novel biomarkers in pancreatic cancer represents a significant advancement in the field. By harnessing the power of artificial intelligence, researchers are poised to make strides towards earlier detection and more effective treatment, ultimately improving patient outcomes in this challenging disease.

Unveiling the microRNA landscape in pancreatic ductal adenocarcinoma patients and cancer cell models

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge due to late-stage diagnoses resulting from nonspecific early symptoms and the absence of early diagnostic biomarkers. MicroRNAs (miRNAs) play a crucial role in regulating diverse biological processes, and their abnormal expression is observed in various diseases, including cancer. Cancer stem cells (CSCs) are thought to act as a driving force in PDAC spread and recurrence. In pursuing the goal of unravelling the complexities of PDAC and its underlying molecular mechanisms, our study aimed to identify PDAC-associated miRNAs and relate them to disease progression, focusing on their involvement in various PDAC stages in patients and in reliable in vitro models, including pancreatic CSC (PaCSC) models.

METHODS

The miRNA profiling datasets of serum and solid biopsies of PDAC patients deposited in GEO DataSets were analyzed by REML-based meta-analysis. The panel was then investigated by Real Time PCR in serum and solid biopsies of 37 PDAC patients enrolled in the study, as well as on BxPC-3 and AsPC-1 PDAC cell lines. We extended our focus towards a possible role of PDAC-associated miRNAs in the CSC phenotype, by inducing CSC-enriched pancreatospheres from BxPC-3 and AsPC-1 PDAC cell lines and performed differential miRNA expression analysis between PaCSCs and monolayer-grown PDAC cell lines.

RESULTS

Meta-analysis showed differentially expressed miRNAs in blood samples and cancerous tissues of PDAC patients, allowing the identification of a panel of 9 PDAC-associated miRNAs. The results emerging from our patients fully confirmed the meta-analysis for the majority of miRNAs under investigation. In vitro tasks confirmed the aberrant expression of the panel of PDAC-associated miRNAs, with a dramatic dysregulation in PaCSC models. Notably, PaCSCs have shown significant overexpression of miR-4486, miR-216a-5p, and miR-216b-5p compared to PDAC cell lines, suggesting the recruitment of such miRNAs in stemness-related molecular mechanisms. Globally, our results showed a dual behaviour of miR-216a-5p and miR-216b-5p in PDAC while miR-4486, miR-361-3p, miR-125a-5p, miR-320d expression changes during the disease suggest they could promote PDAC initiation and progression.

CONCLUSIONS

This study contributed to an enhanced comprehension of the role of miRNAs in the development and progression of PDAC, shedding new light on the miRNA landscape in PDAC and its intricate interplay with CSCs, and providing specific insights useful in the development of miRNA-based diagnostic biomarkers and therapeutic targets.

Computational Screening to Predict MicroRNA Targets in the Flavivirus 3' UTR Genome: An Approach for Antiviral Development

MicroRNAs (miRNAs) are molecules that influence messenger RNA (mRNA) expression levels by binding to the 3' untranslated region (3' UTR) of target genes. Host miRNAs can influence flavivirus replication, either by inducing changes in the host transcriptome or by directly binding to viral genomes. The 3' UTR of the flavivirus genome is a conserved region crucial for viral replication. Cells might exploit this well-preserved region by generating miRNAs that interact with it, ultimately impacting viral replication. Despite significant efforts to identify miRNAs capable of arresting viral replication, the potential of all these miRNAs to interact with the flavivirus 3' UTR is still poorly characterised. In this context, bioinformatic tools have been proposed as a fundamental part of accelerating the discovery of interactions between miRNAs and the 3' UTR of viral genomes. In this study, we performed a computational analysis to reveal potential miRNAs from human and mosquito species that bind to the 3' UTR of flaviviruses. In humans, miR-6842 and miR-661 were found, while in mosquitoes, miR-9-C, miR-2945-5p, miR-11924, miR-282-5p, and miR-79 were identified. These findings open new avenues for studying these miRNAs as antivirals against flavivirus infections.

MicroRNA-155 and its exosomal form: Small pieces in the gastrointestinal cancers puzzle

Gastrointestinal (GI) cancers are common cancers that are responsible for a large portion of global cancer fatalities. Due to this, there is a pressing need for innovative strategies to identify and treat GI cancers. MicroRNAs (miRNAs) are short ncRNAs that can be considered either cancer-causing or tumor-inhibiting molecules. MicroRNA-155, also known as miR-155, is a vital regulator in various cancer types. This miRNA has a carcinogenic role in a variety of gastrointestinal cancers, including pancreatic, colon, and gastric cancers. Since the abnormal production of miR-155 has been detected in various malignancies and has a correlation with increased mortality, it is a promising target for future therapeutic approaches. Moreover, exosomal miR-155 associated with tumors have significant functions in communicating between cells and establishing the microenvironment for cancer in GI cancers. Various types of genetic material, such as specifically miR-155 as well as proteins found in cancer-related exosomes, have the ability to be transmitted to other cells and have a function in the advancement of tumor. Therefore, it is critical to conduct a review that outlines the diverse functions of miR-155 in gastrointestinal malignancies. As a result, we present a current overview of the role of miR-155 in gastrointestinal cancers. Our research highlighted the role of miR-155 in GI cancers and covered critical issues in GI cancer such as pharmacologic inhibitors of miRNA-155, miRNA-155-assosiated circular RNAs, immune-related cells contain miRNA-155. Importantly, we discussed miRNA-155 in GI cancer resistance to chemotherapy, diagnosis and clinical trials. Furthermore, the function of miR-155 enclosed in exosomes that are released by cancer cells or tumor-associated macrophages is also covered.

Data privacy-aware machine learning approach in pancreatic cancer diagnosis

PROBLEM

Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the five-year survival rate can reach up to 70%. Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers.

AIM

The study aims to diagnose a patient's pancreatic cancer while ensuring the confidentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer.

METHODS

Machine learning, a branch of artificial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as filling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confidentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process.

RESULTS

Among the 590 clinical records analyzed, 199 (33.7%) belonged to patients with pancreatic cancer, 208 (35.3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efficiency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86%. In order to understand which features are more critical and which data the model is based on, the analysis found that the features "plasma_CA19_9", REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process.

CONCLUSIONS

This research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.

MicroRNAs in Pancreatic Cancer: Advances in Biomarker Discovery and Therapeutic Implications

Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a case study using public datasets and address commonly identified research discrepancies, challenges, and limitations. Lastly, we discuss analytical approaches that integrate multimodal analyses incorporating clinical and molecular features, presenting new insights into identifying robust miRNA-centric biomarkers.

MicroRNA analysis of Natural Killer cell-derived exosomes: the microRNA let-7b-5p is enriched in exosomes and participates in their anti-tumor effects against pancreatic cancer cells

Natural Killer (NK) cells are important components of the immune system in the defense against tumor growth and metastasis. They release exosomes containing proteins and nucleic acids, including microRNAs (miRNAs). NK-derived exosomes play a role in the anti-tumor NK cell function since they are able to recognize and kill cancer cells. However, the involvement of exosomal miRNAs in the function of NK exosomes is poorly understood. In this study, we explored the miRNA content of NK exosomes by microarray as compared to their cellular counterparts. The expression of selected miRNAs and lytic potential of NK exosomes against childhood B acute lymphoblastic leukemia cells after co-cultures with pancreatic cancer cells were also evaluated. We identified a small subset of miRNAs, including miR-16-5p, miR-342-3p, miR-24-3p, miR-92a-3p and let-7b-5p that is highly expressed in NK exosomes. Moreover, we provide evidence that NK exosomes efficiently increase let-7b-5p expression in pancreatic cancer cells and induce inhibition of cell proliferation by targeting the cell cycle regulator CDK6. Let-7b-5p transfer by NK exosomes could represent a novel mechanism by which NK cells counteract tumor growth. However, both cytolytic activity and miRNA content of NK exosomes were reduced upon co-culture with pancreatic cancer cells. Alteration in the miRNA cargo of NK exosomes, together with their reduced cytotoxic activity, could represent another strategy exerted by cancer to evade the immune response. Our study provides new information on the molecular mechanisms used by NK exosomes to exert anti-tumor-activity and offers new clues to integrate cancer treatments with NK exosomes.

Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks

BACKGROUND

Early diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is the main key to surviving cancer patients. Urine proteomic biomarkers which are creatinine, LYVE1, REG1B, and TFF1 present a promising non-invasive and inexpensive diagnostic method of the PDAC. Recent utilization of both microfluidics technology and artificial intelligence techniques enables accurate detection and analysis of these biomarkers. This paper proposes a new deep-learning model to identify urine biomarkers for the automated diagnosis of pancreatic cancers. The proposed model is composed of one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). It can categorize patients into healthy pancreas, benign hepatobiliary disease, and PDAC cases automatically.

RESULTS

Experiments and evaluations have been successfully done on a public dataset of 590 urine samples of three classes, which are 183 healthy pancreas samples, 208 benign hepatobiliary disease samples, and 199 PDAC samples. The results demonstrated that our proposed 1-D CNN + LSTM model achieved the best accuracy score of 97% and the area under curve (AUC) of 98% versus the state-of-the-art models to diagnose pancreatic cancers using urine biomarkers.

CONCLUSION

A new efficient 1D CNN-LSTM model has been successfully developed for early PDAC diagnosis using four proteomic urine biomarkers of creatinine, LYVE1, REG1B, and TFF1. This developed model showed superior performance on other machine learning classifiers in previous studies. The main prospect of this study is the laboratory realization of our proposed deep classifier on urinary biomarker panels for assisting diagnostic procedures of pancreatic cancer patients.

MicroRNA-Mediated Mitochondrial Dysfunction Is Involved in the Anti-triple-Negative Breast Cancer Cell Activity of Phytosesquiterpene Lactones

Aims: Emerging evidence suggests that modulating redox homeostasis through targeting mitochondrial functions may be a useful strategy for suppressing triple-negative breast cancer (TNBC) activities. However, whether there are specific microRNAs (miRNAs) involved in regulating oxidative stress-associated mitochondrial functions that can act as therapeutic targets to suppress TNBC activities remains unclear. Here, we aimed to identify the role of redox-associated miRNAs in TNBC and investigated their potential as therapeutic targets. Results: We identified oxidative stress-responsive differentially expressed miRNAs (DEMs) regulated by phytosesquiterpene lactone deoxyelephantopin (DET) and its novel derivative DETD-35, which are known to inhibit TNBC growth and metastasis in vitro and in vivo, using comparative miRNA microarray analysis and reactive oxygen species (ROS) scavenging approaches. Mitochondrial dysfunction was identified as a major biological function regulated by a few specific DEMs. In particular, miR-4284 was identified to play a role in DET- and DETD-35-mediated ROS production, mitochondrial basal proton leak, and antiproliferation activity in TNBC cells. Moreover, DET- and DETD-35-induced mitochondrial DNA damage was observed in TNBC cells and xenograft tumors. miR-4284 was also identified to play a role in oxidative DNA damage in TNBC tumors. Innovation: We identified a novel role for miR-4284 in regulating mitochondrial basal proton leak in TNBC cells, and highlighted its significance in TNBC tumor oxidative DNA damage, and its direct correlation with TNBC patient survival. Conclusion: We used DET and DETD-35 as proof of concept to demonstrate that activities of anticancer agents can involve regulation of multiple miRNAs playing different roles in cancer progression. Antioxid. Redox Signal. 38, 198-214.

MicroRNAs and long non-coding RNAs in pancreatic cancer: From epigenetics to potential clinical applications

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two relevant classes of non-coding RNAs (ncRNAs) that play a pivotal role in a number of molecular processes through different epigenetic regulatory mechanisms of gene expression. As a matter of fact, the altered expression of these types of RNAs leads to the development and progression of a varied range of multifactorial human diseases. Several recent reports elucidated that miRNA and lncRNAs have been implicated in pancreatic cancer (PC). For instance, dysregulation of such ncRNAs has been found to be associated with chemoresistance, apoptosis, autophagy, cell differentiation, tumor suppression, tumor growth, cancer cell proliferation, migration, and invasion in PC. Moreover, several aberrantly expressed miRNAs and lncRNAs have the potential to be used as biomarkers for accurate PC diagnosis. Additionally, miRNAs and lncRNAs are considered as promising clinical targets for PC. Therefore, in this review, we discuss recent experimental evidence regarding the clinical implications of miRNAs and lncRNAs in the pathophysiology of PC, their future potential, as well as the challenges that have arisen in this field of study in order to drive forward the design of ncRNA-based diagnostics and therapeutics for PC.

The Emerging Role of MicroRNAs and Autophagy Mechanism in Pancreatic Cancer Progression: Future Therapeutic Approaches

Pancreatic cancer constitutes the fourth most frequent cause of death due to malignancy in the US. Despite the new therapeutic modalities, the management of pancreatic ductal adenocarcinoma (PDAC) is considered a difficult task for clinicians due to the fact that is usually diagnosed in already advanced stages and it is relatively resistant to the current chemotherapeutic agents. The molecular background analysis of pancreatic malignant tumors, which includes various epigenetic and genetic alterations, opens new horizons for the development of novel diagnostic and therapeutic strategies. The interplay between miRNAs, autophagy pathway, and pancreatic carcinogenesis is in the spotlight of the current research. There is strong evidence that miRNAs take part in carcinogenesis either as tumor inhibitors that combat the oncogene expression or as promoters (oncomiRs) by acting as oncogenes by interfering with various cell functions such as proliferation, programmed cell death, and metabolic and signaling pathways. Deregulation of the expression levels of various miRNAs is closely associated with tumor growth, progression, and dissemination, as well as low sensitivity to chemotherapeutic agents. Similarly, autophagy despite constituting a pivotal homeostatic mechanism for cell survival has a binary role in PDAC, either as an inhibitor or promoter of carcinogenesis. The emerging role of miRNAs in autophagy gets a great deal of attention as it opens new opportunities for the development of novel therapeutic strategies for the management of this aggressive and chemoresistant malignancy. In this review, we will shed light on the interplay between miRNAs and the autophagy mechanism for pancreatic cancer development and progression.

Advances in biomarkers and techniques for pancreatic cancer diagnosis

Pancreatic cancer is the most lethal type of malignancy and is characterized by high invasiveness without severe symptoms. It is difficult to detect PC at an early stage because of the low diagnostic accuracy of existing routine methods, such as abdominal ultrasound, CT, MRI, and endoscopic ultrasound (EUS). Therefore, it is of value to develop new diagnostic techniques for early detection with high accuracy. In this review, we aim to highlight research progress on novel biomarkers, artificial intelligence, and nanomaterial applications on the diagnostic accuracy of pancreatic cancer.

Fast and robust imputation for miRNA expression data using constrained least squares

BACKGROUND

High dimensional transcriptome profiling, whether through next generation sequencing techniques or high-throughput arrays, may result in scattered variables with missing data. Data imputation is a common strategy to maximize the inclusion of samples by using statistical techniques to fill in missing values. However, many data imputation methods are cumbersome and risk introduction of systematic bias.

RESULTS

We present a new data imputation method using constrained least squares and algorithms from the inverse problems literature and present applications for this technique in miRNA expression analysis. The proposed technique is shown to offer an imputation orders of magnitude faster, with greater than or equal accuracy when compared to similar methods from the literature.

CONCLUSIONS

This study offers a robust and efficient algorithm for data imputation, which can be used, e.g., to improve cancer prediction accuracy in the presence of missing data.